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An efficient multimodal face recognition method robust to pose variation

机译:鲁棒的姿态变化有效的多模式人脸识别方法

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In the recent years, face recognition has obtained much attention. Using combined 2D and 3D face recognition is an alternative method to deal with face recognition. A novel multimodal face recognition algorithm based on Gabor wavelet information is presented in this paper. The Principal Component Analysis (PCA) and the Linear Discriminant analysis (LDA) have been used for size reduction. The system has combined 2D and 3D systems in the decision level which presents higher performance in contrast with methods which use only 2D and 3D systems, separately. The proposed algorithm is examined with FRAV3D database that has faces with pose variation and 95% performance that is achieved in rank-one for fusion experiment.
机译:近年来,人脸识别备受关注。使用组合的2D和3D人脸识别是处理人脸识别的另一种方法。提出了一种基于Gabor小波信息的新型多模态人脸识别算法。主成分分析(PCA)和线性判别分析(LDA)已用于减小尺寸。该系统在决策层结合了2D和3D系统,与单独使用2D和3D系统的方法相比,该系统具有更高的性能。该算法在FRAV3D数据库中进行了检查,该数据库具有姿态变化和95%的性能,可在融合实验中获得第一名。

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